Using Cloud Trace on Kubernetes Engine
When supporting a production system that services HTTP requests or provides an API, it is important to measure the latency of your endpoints to detect when a system's performance is not operating within specification. In monolithic systems this single latency measure may be useful to detect and diagnose deteriorating behavior. With modern microservice architectures, however, this becomes much more difficult because a single request may result in numerous additional requests to other systems before the request can be fully handled. Deteriorating performance in an underlying system may impact all other systems that rely on it. While latency can be measured at each service endpoint, it can be difficult to correlate slow behavior in the public endpoint with a particular sub-service that is misbehaving.
Enter distributed tracing. Distributed tracing uses metadata passed along with requests to correlate requests across service tiers. By collecting telemetry data from all the services in a microservice architecture and propagating a trace id from an initial request to all subsidiary requests, developers can much more easily identify which service is causing slowdowns affecting the rest of the system.
Google Cloud provides the Operations suite of products to handle logging, monitoring, and distributed tracing. This lab will be deployed to Kubernetes Engine and will demonstrate a multi-tier architecture implementing distributed tracing. It will also take advantage of Terraform to build out necessary infrastructure.
This lab was created by GKE Helmsman engineers to give you a better understanding of GKE Binary Authorization. You can view this demo on Github here. We encourage any and all to contribute to our assets!
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- Google Cloud Console에 대한 임시 액세스 권한을 얻습니다.
- 초급부터 고급 수준까지 200여 개의 실습이 준비되어 있습니다.
- 자신의 학습 속도에 맞춰 학습할 수 있도록 적은 분량으로 나누어져 있습니다.
Use Terraform to set up the necessary infrastructure
Create Cloud Monitoring workspace
Deploy demo application
Generate Telemetry Data